808 research outputs found
Physical-layer Network Coding: A Random Coding Error Exponent Perspective
In this work, we derive the random coding error exponent for the uplink phase
of a two-way relay system where physical layer network coding (PNC) is
employed. The error exponent is derived for the practical (yet sub-optimum) XOR
channel decoding setting. We show that the random coding error exponent under
optimum (i.e., maximum likelihood) PNC channel decoding can be achieved even
under the sub-optimal XOR channel decoding. The derived achievability bounds
provide us with valuable insight and can be used as a benchmark for the
performance of practical channel-coded PNC systems employing low complexity
decoders when finite-length codewords are used.Comment: Submitted to IEEE International Symposium on Information Theory
(ISIT), 201
Bit-Interleaved Coded Modulation Revisited: A Mismatched Decoding Perspective
We revisit the information-theoretic analysis of bit-interleaved coded
modulation (BICM) by modeling the BICM decoder as a mismatched decoder. The
mismatched decoding model is well-defined for finite, yet arbitrary, block
lengths, and naturally captures the channel memory among the bits belonging to
the same symbol. We give two independent proofs of the achievability of the
BICM capacity calculated by Caire et al. where BICM was modeled as a set of
independent parallel binary-input channels whose output is the bitwise
log-likelihood ratio. Our first achievability proof uses typical sequences, and
shows that due to the random coding construction, the interleaver is not
required. The second proof is based on the random coding error exponents with
mismatched decoding, where the largest achievable rate is the generalized
mutual information. We show that the generalized mutual information of the
mismatched decoder coincides with the infinite-interleaver BICM capacity. We
also show that the error exponent -and hence the cutoff rate- of the BICM
mismatched decoder is upper bounded by that of coded modulation and may thus be
lower than in the infinite-interleaved model. We also consider the mutual
information appearing in the analysis of iterative decoding of BICM with EXIT
charts. We show that the corresponding symbol metric has knowledge of the
transmitted symbol and the EXIT mutual information admits a representation as a
pseudo-generalized mutual information, which is in general not achievable. A
different symbol decoding metric, for which the extrinsic side information
refers to the hypothesized symbol, induces a generalized mutual information
lower than the coded modulation capacity.Comment: submitted to the IEEE Transactions on Information Theory. Conference
version in 2008 IEEE International Symposium on Information Theory, Toronto,
Canada, July 200
Complex Block Floating-Point Format with Box Encoding For Wordlength Reduction in Communication Systems
We propose a new complex block floating-point format to reduce implementation
complexity. The new format achieves wordlength reduction by sharing an exponent
across the block of samples, and uses box encoding for the shared exponent to
reduce quantization error. Arithmetic operations are performed on blocks of
samples at time, which can also reduce implementation complexity. For a case
study of a baseband quadrature amplitude modulation (QAM) transmitter and
receiver, we quantify the tradeoffs in signal quality vs. implementation
complexity using the new approach to represent IQ samples. Signal quality is
measured using error vector magnitude (EVM) in the receiver, and implementation
complexity is measured in terms of arithmetic complexity as well as memory
allocation and memory input/output rates. The primary contributions of this
paper are (1) a complex block floating-point format with box encoding of the
shared exponent to reduce quantization error, (2) arithmetic operations using
the new complex block floating-point format, and (3) a QAM transceiver case
study to quantify signal quality vs. implementation complexity tradeoffs using
the new format and arithmetic operations.Comment: 6 pages, 9 figures, submitted to Asilomar Conference on Signals,
Systems, and Computers 201
Efficient joint maximum-likelihood channel estimation and signal detection
In wireless communication systems, channel state information is often assumed to be available at the receiver. Traditionally, a training sequence is used to obtain the estimate of the channel. Alternatively, the channel can be identified using known properties of the transmitted signal. However, the computational effort required to find the joint ML solution to the symbol detection and channel estimation problem increases exponentially with the dimension of the problem. To significantly reduce this computational effort, we formulate the joint ML estimation and detection as an integer least-squares problem, and show that for a wide range of signal-to-noise ratios (SNR) and problem dimensions it can be solved via sphere decoding with expected complexity comparable to the complexity of heuristic
techniques
- âŠ